In the following exercises, we will use the data you have collected in the previous session (all comments for the video “The Census” by Last Week Tonight with John Oliver. Please note that your results might look slightly different than the output in the solutions for these exercises as we collected the comments earlier.

First we need to load the parsed comments data (NB: You might have to adjust the following code to use the correct file path on your computer).

comments <- readRDS("../data/ParsedComments.rds")

After loading the data, we go through the preprocessing steps described in the slides. In a first step, we remove newline commands from the comment strings (without emojis).

library(tidyverse)

comments <- comments %>% 
  mutate(TextEmojiDeleted = str_replace_all(TextEmojiDeleted,
                                            pattern = "\\\n",
                                            replacement = " "))

Next, we tokenize the comments and create a document-feature matrix from which we remove English stopwords.

library(quanteda)

toks <- comments %>% 
  pull(TextEmojiDeleted) %>% 
  char_tolower() %>% 
  tokens(remove_numbers = TRUE,
               remove_punct = TRUE,
               remove_separators = TRUE,
               remove_symbols = TRUE,
               split_hyphens = TRUE,
               remove_url = TRUE)

comments_dfm <- dfm(toks, 
                   remove = quanteda::stopwords("english"))

1

Which are the 20 most frequently used words in the comments on the video “The Census” by Last Week Tonight with John Oliver? Save the overall word ranking in a new object called term_freq.

You can use the function textstat_frequency() from the quanteda package to answer this question.

solution 

term_freq <- textstat_frequency(comments_dfm)
head(term_freq, 20)
##       feature frequency rank docfreq group
## 1      census      1820    1    1398   all
## 2      people      1011    2     739   all
## 3        just       763    3     659   all
## 4        like       620    4     524   all
## 5         one       525    5     441   all
## 6         can       500    6     437   all
## 7       trump       494    7     443   all
## 8        know       458    8     405   all
## 9         get       442    9     394   all
## 10       john       431   10     401   all
## 11 government       391   11     314   all
## 12   question       374   12     318   all
## 13         us       371   13     307   all
## 14       many       363   14     310   all
## 15   citizens       359   15     260   all
## 16    country       296   16     248   all
## 17       even       295   17     274   all
## 18      think       293   18     268   all
## 19       want       282   19     243   all
## 20    illegal       281   20     216   all

2

Instead of the raw frequency we can also look at the number of comments that a particular word appears in. This metric takes into account that words might be used multiple times in the same comment. What are the 10 words that are used in the highest number of comments on the video “The Census” by Last Week Tonight with John Oliver?

You should use the variable docfreq from the term_freq object you created in the previous task.

solution 

term_freq  %>% 
  arrange(-docfreq) %>% 
  head(10)
##    feature frequency rank docfreq group
## 1   census      1820    1    1398   all
## 2   people      1011    2     739   all
## 3     just       763    3     659   all
## 4     like       620    4     524   all
## 7    trump       494    7     443   all
## 5      one       525    5     441   all
## 6      can       500    6     437   all
## 8     know       458    8     405   all
## 10    john       431   10     401   all
## 9      get       442    9     394   all

We also want to look at the emojis that were used in the comments on the video “The Census” by Last Week Tonight with John Oliver. Similar to what we did for the comment text without emojis, we first need to wrangle the data (remove missings, tokenize emojis, create DFM).

emoji_toks <- comments %>% 
  mutate(Emoji = na_if(Emoji, "NA")) %>% 
  mutate (Emoji = str_trim(Emoji)) %>%
  filter(!is.na(Emoji)) %>%
  pull(Emoji) %>% 
  tokens(what = "fastestword")

EmojiDfm <- dfm(emoji_toks)

3

What were the 10 most frequently used emojis comments on the video “The Census” by Last Week Tonight with John Oliver?

The solution is essentially the same as the one for the first task in this exercise (word frequencies).

solution 

EmojiFreq <- textstat_frequency(EmojiDfm)
head(EmojiFreq, n = 10)
##                            feature frequency rank docfreq group
## 1         emoji_facewithtearsofjoy       103    1      60   all
## 2  emoji_rollingonthefloorlaughing        37    2      21   all
## 3               emoji_thinkingface        29    3      18   all
## 4      emoji_grinningfacewithsweat        15    4      13   all
## 5                 emoji_registered        14    5       4   all
## 6                       emoji_fire        12    6       3   all
## 7      emoji_grinningsquintingface        10    7       6   all
## 8           emoji_loudlycryingface        10    7       6   all
## 9               emoji_unamusedface         9    9       9   all
## 10             emoji_clappinghands         8   10       2   all

4

The ranking based on raw counts of emojis might be affected by YouTube users “spamming” emojis in their comments (i.e., using the same emojis many times in the same comment). Hence, it makes sense to also look at the number of unique comments that an emoji appears in. What are the 10 emojis that appear in the highest number of comments on the video “The Census” by Last Week Tonight with John Oliver?

The solution is essentially the same as the one for the second task in this exercise (docfreq for words).

solution 

EmojiFreq  %>% 
  arrange(-docfreq) %>% 
  head(10)
##                            feature frequency rank docfreq group
## 1         emoji_facewithtearsofjoy       103    1      60   all
## 2  emoji_rollingonthefloorlaughing        37    2      21   all
## 3               emoji_thinkingface        29    3      18   all
## 4      emoji_grinningfacewithsweat        15    4      13   all
## 9               emoji_unamusedface         9    9       9   all
## 12       emoji_facewithrollingeyes         7   11       7   all
## 13                  emoji_thumbsup         7   11       7   all
## 7      emoji_grinningsquintingface        10    7       6   all
## 8           emoji_loudlycryingface        10    7       6   all
## 11 emoji_smilingfacewithsunglasses         7   11       6   all

Bonus

If you’re finished with tasks 1-4 and/or want to do/try out something else, you can create an emoji plot similar to the one you saw in the lecture slides for the video “The Census” by Last Week Tonight with John Oliver. We have created a script containing a function for the emoji mapping which you can source with the following code (NB: you might have to adjust the path to the script in the code below). You might also want to have a look at the emoji_mapping_function.R file to see what this functions does. 
source("../Scripts/emoji_mapping_function.R")

You need to add the mapping objects to your plot. To see how you can construct the plot, you can have a look at slide #31 from the session on Basic Text Analysis of User Comments.

solution 

create_emoji_mappings(EmojiFreq, 10)

EmojiFreq %>% 
head(n = 10) %>% 
ggplot(aes(x = reorder(feature, -frequency), y = frequency)) +
  geom_bar(stat="identity",
           color = "black",
           fill = "#FF74A6",
           alpha = 0.7) + 
  geom_point() +
  labs(title = "Most frequent emojis in comments",
       subtitle = "The Census: Last Week Tonight with John Oliver (HBO)
       \nhttps://www.youtube.com/watch?v=1aheRpmurAo",
       x = "",
       y = "Frequency") +
  scale_y_continuous(expand = c(0,0),
                     limits = c(0,150)) +
  theme(panel.grid.major.x = element_blank(),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank()) +
  mapping1 +
  mapping2 +
  mapping3 +
  mapping4 +
  mapping5 +
  mapping6 +
  mapping7 +
  mapping8 +
  mapping9 +
  mapping10